Features and functionality.

Below is a high level overview of server functions and features. For more detailed specifications, please contact us.

Feature	Description

Vocabulary Builder	The Vocabulary Builder has two main sections and one primary function. The sections are the Core Vocabulary and the custom built Domain Vocabulary. The primary function is automatically building and automatically updating the Vocabulary words, phrases and associated topic sets.
Core Vocabulary	Provided with the Emantix server is a Core Vocabulary of approximately 140,000 words and word meanings. Each word has an average set of 1,500 topics ordered by popularity, or frequency of occurrence. This Core Vocabulary is managed and maintained by Emantix with frequent updates pushed out to installed appliances. Size: 60 GB.
Domain Vocabulary	The Domain Vocabulary uses the Core Vocabulary as a baseline to search internal documents and extract, match and value additional topics and phrases determined to be relevant and meaningful. Size ranges from 100GB to 200GB.
Search Engine	While Emantix supports other search engines for search and retrieval, it also has its own search/index/crawler so that sufficient internal content can be analyzed to provide the Domain Vocabulary the quantity of data it needs to operate at maximum efficiency. Number of documents retrieved is configurable.
Meta Search Engine	For environments that have multiple search systems in place, the Meta Search Engine can be used to effectively search them all to populate the Domain Vocabulary.

Vocabulary Administrator	The Vocabulary Administrator provides tools for managing the vocabularies as well as system system configuration and administration.
Vocabulary Editor	This interface provides for the manual addition, removal and modification of Domain and Core Vocabulary words and phrases and associated topics.
Thesaurus Editor	This interface provides for the manual addition, removal and modification of words, word definitions and linguistic relationships.
Analytics	The hybrid semantic-statistic analytics engine parses content, correlates with the vocabularies and produces output for the web service.
Content Distiller	The Content Distiller ingests and parses unstructured text of any length or size. The Content Distiller prepares the content for output to the Web Service. The Content Distiller uses semantic and statistic methods to evaluate the words in the source content, eliminating words with little value and elevating words of greater value as determined by frequency of occurrence, word definition confidence, and topic richness.
Content Expander	The Content Expander does the reverse of the Content Distiller – instead of reducing a large amount of information down to essential words and concepts, it takes a small amount of information (a caption or blurb, for example) and expands linguistically and conceptually so that there is more relevant data for search and retrieval. The caption service can be called from direct input where the user can set parameters, or it can be done as a query against database tables, where global parameters are set for each database queried.
Web Service	The Emantix web service API accepts unstructured text and provides four outputs. These outputs can be used to create custom APIs for application integration or for standalone applications.
Content Input	Content Input ingests text, HTML or XML from a URI source. This can be an online document or application, website or database query.
Meaning Output	Meaning Output provides precise word definitions for input content. This could be used to build custom dictionaries or glossaries.
Relevance Output	Relevance Output provides a ranking of words from most to least relevant, word relevance defined by frequency of occurrence, word definition confidence, and topic richness.
Context Output	Context Output provides a word set that defines the overall intent, or context of the document. These can be used for search queries, tag clouds, etc.
Concepts Output	Concepts Output provides topics ranked from high to low relevance, topic relevance determined by topic occurrence ranking in corpus senses and total count in source content.